Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.

Spontaneous bilateral tubal ectopic pregnancy preoperatively diagnosed by the ultrasound: a case report.

BACKGROUND: Bilateral ectopic pregnancy is extremely rare, with a tremendous maternal mortality and morbidity risk, requiring rapid diagnosis and management. This condition is usually diagnosed during surgery, as radiologists may not pay enough attention to the contralateral side of interest. Therefore, reminding of this rare but emergent situation can be beneficial for both radiologists and gynecologists. Here we report a case of bilateral ectopic pregnancy, which was first diagnosed with ultrasound and was confirmed during laparoscopy. CASE PRESENTATION: A 34 years old woman complaining of light vaginal bleeding at 6 weeks of gestation by her last menstrual period presented to our institute. The serum ß-HCG levels were analyzed and followed during patient's admission. Unfortunately, serum levels weren't decreasing and blood test titration before surgery were as: 851,894,975 IU/l (checked daily and not every 48 h because of patient's status and being bilateral). There was no evidence of intrauterine pregnancy at the transvaginal ultrasound, but heterogeneous adnexal masses were seen at both adnexa, suspected of bilateral ectopic pregnancy. She underwent laparoscopic exploration, which confirmed the diagnosis. Bilateral salpingostomy was done to preserve fertility, and the patient's recovery was uneventful. CONCLUSIONS: Even with a unilateral report of ectopic pregnancy preoperatively in ultrasonography, surgeons should always be aware of the probability of bilateral ectopic pregnancies anytime facing susceptible cases, especially in patients with known risk factors. Also, it is an important reminder for radiologists to check both adnexa when facing a unilateral adnexal mass resembling ectopic pregnancy.

Time course study of optical coherence tomography angiography in patients with methanol induced optic neuropathy.

INTRODUCTION: In countries where alcoholic beverages are legally prohibited, methanol toxicity usually occurs due to ingesting homemade alcoholic drinks. The initial ophthalmologic symptoms of methanol toxicity typically appear 6-48 h after ingestion, and the severity of symptoms varies widely from mild and painless decreased vision to no-light perception vision. METHODS: This prospective study examines 20 patients with acute methanol poisoning within 10 days of use. Patients underwent ocular examinations, BCVA (Best Corrected Visual Acuity) recording, and OCTA (Optical Coherence Tomography Angiography) of the macula and optic disc. BCVA measurement and imaging were repeated one month and three months after intoxication. RESULTS: There was a statistically significant reduction in superficial parafoveal vascular density (P-value = 0.026), inner retinal thickness (P-value = 0.022), RNFL (Retinal Nerve Fiber Layer) thickness (P-value = 0.031), and an increase in cup to disc ratio (P-value < 0.001), and central visual acuity (P-value = 0.002) in this time course. However, there was no statistically significant difference in FAZ (Foveal Avascular Zone) area (P-value = 0.309), FAZ perimeter (P-value = 0.504), FD-300 (Foveal density, vascular density within a 300 µm wide region of the FAZ) (P-value = 0.541), superficial vascular density (P-value = 0.187), deep foveal vascular density (P-value = 0.889), deep parafoveal vascular density (P-value = 0.830), choroidal flow area (P-value = 0.464), total retinal thickness (P-value = 0.597), outer retinal thickness (P-value = 0.067), optic disc whole image vascular density (P-value = 0.146), vascular density inside the disc (P-value = 0.864), or peripapillary vascular density (P-value = 0.680) at different times. CONCLUSION: Over time, methanol poisoning can cause changes in retinal layers thickness, vasculature, and optic nerve head. The most important changes include cupping of the optic nerve head, reduction in RNFL thickness, and inner retinal thickness.

Evaluating macular and optic nerve changes during Ramadan fasting by optical coherence tomography angiography-a prospective before-and-after study.

PURPOSE: During Ramadan, millions of Muslims fast from sunrise to sunset. Fasting influences the hormonal system, sympathetic activity, blood circulation, and metabolic pathways. Because of these changes, we employed optical coherence tomography angiography (OCTA) to investigate the effects of fasting on the macula and optic nerve. METHODS: In this prospective before-and-after study, both eyes of 45 participants were examined. Vascular characteristics of the macula and optic nerve head were evaluated in the morning and evening, once before Ramadan and once on the 20th day of Ramadan. RESULTS: Compared to the non-fasting condition, fasting significantly reduced inner parafoveal retinal thickness in both eyes and total foveal retinal thickness in the left eye in the morning and evening. Fasting in the morning also considerably reduced inner foveal retinal thickness in both eyes and total parafoveal retinal thickness in the right eye. Fasting significantly reduced central choroidal flow (1 mm) in both eyes in the evening (all p-values are < 0.05). In the morning, there were significant increases in the foveal avascular zone (FAZ) area (p-value = 0.006) and deep parafoveal vascular density in the left eye (p-value = 0.001). CONCLUSION: Fasting alters both the macular characteristics and the optic nerve head, as seen in OCTA, although it did not affect participants' vision. However, further research is needed before reaching a broad conclusion.

Methanol Induced Optic Neuropathy: Molecular Mysteries, Public Health Perspective, Clinical Insights and Treatment Strategies.

Methanol-induced optic neuropathy (MION) represents a critical public health issue, particularly prevalent in lower socioeconomic populations and regions with restricted alcohol access. MION, characterized by irreversible visual impairment, arises from the toxic metabolization of methanol into formaldehyde and formic acid, leading to mitochondrial oxidative phosphorylation inhibition, oxidative stress, and subsequent neurotoxicity. The pathogenesis involves axonal and glial cell degeneration within the optic nerve and potential retinal damage. Despite advancements in therapeutic interventions, a significant proportion of affected individuals endure persistent visual sequelae. The study comprehensively investigates the pathophysiology of MION, encompassing the absorption and metabolism of methanol, subsequent systemic effects, and ocular impacts. Histopathological changes, including alterations in retinal layers and proteins, Müller cell dysfunction, and visual symptoms, are meticulously examined to provide insights into the disease mechanism. Furthermore, preventive measures and public health perspectives are discussed to highlight the importance of awareness and intervention strategies. Therapeutic approaches, such as decontamination procedures, ethanol and fomepizole administration, hemodialysis, intravenous fluids, electrolyte balance management, nutritional therapy, corticosteroid therapy, and erythropoietin (EPO) treatment, are evaluated for their efficacy in managing MION. This comprehensive review underscores the need for increased awareness, improved diagnostic strategies, and more effective treatments to mitigate the impact of MION on global health.

Uveal metastasis arising from thyroid neoplasms: A case study synthesis.

PURPOSE: We systematically reviewed the case report literature to identify cases of uveal metastases originating from thyroid cancer (TC), evaluate factors and indications in uveal metastases from TC, and provide clinical insights through recent case studies. METHODS: Web of Science, Medline, and Scopus databases were searched for case reports or series reporting uveal metastasis from a thyroid neoplasm. Articles published in any language from inception through November 2022 were searched and screened independently by two reviewers. The quality of the included studies was assessed using the JBI Critical Appraisal Checklist for Case Reports. RESULTS: A total of 1049 records were screened, resulting in the identification of 46 cases from 43 studies. The mean (SD) age at uveal metastases diagnosis was 58.44 (±17.99) years with the median (interquartile range) of 56.5 (29.75) (range, 20-83 years), with 34.8% of cases (16/46) cases reported in elderly patients (>64 years). The sample consisted of 56.5% (26/46) male patients. Uveal metastases were observed in the right eye in 16 cases, the left eye in 19 cases, and both eyes in 11 cases. Choroidal involvement was present in 84.8% of cases (39/46) cases. Papillary carcinoma was the most common thyroid cancer type (34.8%, 16/46), followed by follicular carcinoma (32.6%, 15/46), and medullary carcinoma (21.7%, 10/46). CONCLUSION: Uveal metastases have been observed to appear in metastatic TC, and physicians should approach ocular symptoms cautiously in cases that accompany a neck mass or a history of previous TC.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

Probing and nasolacrimal intubation outcomes in children over 18 Months of age with congenital nasolacrimal duct obstruction.

Purpose: To evaluate how risk factors impact success rates of initial probing and nasolacrimal duct (NLD) tube intubation in children over 18 months of age with congenital nasolacrimal duct obstruction (CNLDO). Methods: This cohort study included 98 CNLDO patients aged 18 months to 10 years who underwent NLD probing with stent insertion. We employed the multivariate frailty model as our final model to conceptually elaborate on our correlated eye data, with the primary outcome measure evaluating the success rates of probing and tube intubation. Factors such as age, probing complexity, tube type, prior surgeries, and passive smoking were considered in the evaluation. Results: The study involved 98 patients (54 males, 44 females) with a mean age of 41.46 months and an average follow-up of 98.37 days (95 % CI 87.65-109.1). Out of the 110 eyes that underwent surgery, 13 (11.8 %) experienced failure while 97 (88.2 %) were censored. Kaplan-Meier analysis indicated significant differences in age category and probing (P-value = 0.03 and 0.006 respectively), but not tube type (P-value = 0.8). Multivariable analysis confirmed that older age and complex probing were associated with higher failure rates in CNLDO cases, with each monthly increase correlating to a two percent higher likelihood of intubation failure. Conclusions: Patient age and probing complexity influence CNLDO treatment, impacting surgical techniques and outcomes. Tube type, prior surgery, and passive smoking have no significant impact on treatment success.

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls.

Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.

Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English-language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.